Electrocardiogram signal denoising using non‐local wavelet transform domain filtering

Abstract

Electrocardiogram (ECG) signals are usually corrupted by baseline wander, power-line interference, muscle noise etc. Numerous methods have been proposed to remove these noises. However, in case of wireless recording of the ECG signal it gets corrupted by the additive white Gaussian noise (AWGN). For the correct diagnosis, removal of AWGN from ECG signals becomes necessary as it affects the diagnostic features. The natural signals exhibit correlation among their samples and this property has been exploited in various signal restoration tasks. Motivated by that, in this study we propose a non-local wavelet transform domain ECG signal denoising method which exploits the correlations among both local and non-local samples of the signal. In the proposed method, the similar blocks of the samples are grouped in a matrix and then denoising is achieved by the shrinkage of its two-dimensional discrete wavelet transform coefficients. The experiments performed on a number of ECG signals show significant quantitative and qualitative improvements in denoising performance over the existing ECG signal denoising methods.

Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis

“…Kindermann, Osher and Jones applied non-local technique for image deblurring and denoising [18]. Yadav, Sinha and Bora devised non-local wavelet transform domain denoising for ECG signals [19]. NLmeans has been successful applied in the field of mechanical fault diagnosis as well.…”

The Application of Improved Tracking-differentiator Filter in ECG Data

“…Lastly, we get the compensated signal which also is the approximate value 1 ( ) y t of the real value 1 ( ) y t .…”

“…Secondly, we get the output signals 1 ( ) i x t (the tracking signals) and i -order differential signals 2 ( ) i x t from DNTD at 1 t , where 1 ( ) i x t is the filter and approximation of ( 1) , 1,2, ,…”

“…Therefore, it is an important task of ECG signal processing by using digital filtering technology to filter out the various interferences and to obtain accurate detection of ECG signals. It is known that there are many denoising methods have been proposed, the most useful technique is wavelet denoising method [1][2][3]. But there exist some limitations in the wavelet method, such as lack of shift invariance, poor direction selectivity [3][4].…”

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